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            <article class="content wrap" id="_content" data-uid="TensorFlow.TFCode">
  
  
  <h1 id="TensorFlow_TFCode" data-uid="TensorFlow.TFCode">Enum TFCode
  </h1>
  <div class="markdown level0 summary"><p>Status code for invoking a tensorflow operation.</p>
</div>
  <div class="markdown level0 conceptual"></div>
  <h6><strong>Namespace</strong>: <a class="xref" href="../TensorFlow.html">TensorFlow</a></h6>
  <h6><strong>Assembly</strong>: TensorFlowSharp.dll</h6>
  <h5 id="TensorFlow_TFCode_syntax">Syntax</h5>
  <div class="codewrapper">
    <pre><code class="lang-csharp hljs">public enum TFCode</code></pre>
  </div>
  <h3 id="fields">Fields
  </h3>
  <table class="table table-bordered table-striped table-condensed">
    <thead>
      <tr>
        <th>Name</th>
        <th>Description</th>
      </tr>
    <thead>
    <tbody>
      <tr>
        <td id="TensorFlow_TFCode_Aborted">Aborted</td>
        <td><p>The operation was aborted, typically due to a concurrency issue
             like sequencer check failures, transaction aborts, etc.</p>
<pre><code>         See litmus test above for deciding between FailedPrecondition,
         Aborted and Unavailable
</code></pre></td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_AlreadyExists">AlreadyExists</td>
        <td><p>Some entity that we attempted to create (e.g., file or directory) already exists.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Cancelled">Cancelled</td>
        <td><p>The operation was cancelled (typically by the caller).</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_DataLoss">DataLoss</td>
        <td><p>Unrecoverable data loss or corruption.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_DeadlineExceeded">DeadlineExceeded</td>
        <td><p>Deadline expired before operation could complete.  For operations
            that change the state of the system, this error may be returned
            even if the operation has completed successfully.  For example, a
            successful response from a server could have been delayed long
            enough for the deadline to expire.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_FailedPrecondition">FailedPrecondition</td>
        <td><p>Operation was rejected because the system is not in a state
             required for the operation&#39;s execution.  For example, directory
             to be deleted may be non-empty, an rmdir operation is applied to
             a non-directory, etc.</p>
<pre><code>         A litmus test that may help a service implementor in deciding
         between FailedPrecondition, Aborted, and Unavailable:

          (a) Use Unavailableif the client can retry just the failing call.
          (b) Use Aborted if the client should retry at a higher-level
              (e.g., restarting a read-modify-write sequence).
          (c) Use FailedPrecondition if the client should not retry until
              the system state has been explicitly fixed.  E.g., if an &quot;rmdir&quot;
              fails because the directory is non-empty, FailedPrecondition
              should be returned since the client should not retry unless
              they have first fixed up the directory by deleting files from it.
          (d) Use FailedPrecondition if the client performs conditional
              REST Get/Update/Delete on a resource and the resource on the
              server does not match the condition. E.g., conflicting
              read-modify-write on the same resource.
</code></pre></td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Internal">Internal</td>
        <td><p>Internal errors.  Means some invariants expected by underlying
            system has been broken.  If you see one of these errors,
            something is very broken.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_InvalidArgument">InvalidArgument</td>
        <td><p>Client specified an invalid argument.  Note that this differs
            from FailedPrecondition.  InvalidArgumentindicates arguments
            that are problematic regardless of the state of the system
            (e.g., a malformed file name).</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_NotFound">NotFound</td>
        <td><p>Some requested entity (e.g., file or directory) was not found.
            For privacy reasons, this code may be returned when the client
            does not have the access right to the entity.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Ok">Ok</td>
        <td><p>Not an error; returned on success</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_OutOfRange">OutOfRange</td>
        <td><p>Operation tried to iterate past the valid input range.  E.g., seeking or
             reading past end of file.</p>
<pre><code>         Unlike InvalidArgument, this error indicates a problem that may
         be fixed if the system state changes. For example, a 32-bit file
         system will generate InvalidArgument if asked to read at an
         offset that is not in the range [0,2^32-1], but it will generate
         OutOfRange if asked to read from an offset past the current
         file size.

         There is a fair bit of overlap between FailedPrecondition and
         OutOfRange.  We recommend using OutOfRane (the more specific
         error) when it applies so that callers who are iterating through
         a space can easily look for an OutOfRange error to detect when
         they are done.
</code></pre></td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_PermissionDenied">PermissionDenied</td>
        <td><p>The caller does not have permission to execute the specified
            operation.  PermissionDenied must not be used for rejections
            caused by exhausting some resource (use ResourceExhausted
            instead for those errors).  PermissionDeniedmust not be
            used if the caller can not be identified (use Unauthenticated
            instead for those errors).</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_ResourceExhausted">ResourceExhausted</td>
        <td><p>Some resource has been exhausted, perhaps a per-user quota, or
            perhaps the entire file system is out of space.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Unauthenticated">Unauthenticated</td>
        <td><p>The request does not have valid authentication credentials for the
            operation.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Unavailable">Unavailable</td>
        <td><p>The service is currently unavailable.  This is a most likely a
             transient condition and may be corrected by retrying with
             a backoff.</p>
<pre><code>         See litmus test above for deciding between FailedPrecondition,
         Aborted, and Unavailable.
</code></pre></td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Unimplemented">Unimplemented</td>
        <td><p>Operation is not implemented or not supported/enabled in this service.</p>
</td>
      </tr>
      <tr>
        <td id="TensorFlow_TFCode_Unknown">Unknown</td>
        <td><p>Unknown error.  An example of where this error may be returned is
            if a Status value received from another address space belongs to
            an error-space that is not known in this address space.  Also
            errors raised by APIs that do not return enough error information
            may be converted to this error.</p>
</td>
      </tr>
    </tbody>
  </thead></thead></table>
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